The Influence of Neuronal States on Perception and Behavior Unlike reflexes, most of our behaviors are governed by internal states such as satiety or arousal that influence how we respond to our environment. The ability to regulate our internal state in response to experience is immediately evident in mental pathologies where this process is broken. In maladaptive disorders such as Attention Hyperactivity Deficit Disorder (ADHD) or Post-Traumatic Stress Disorder (PTSD), the brain’s ability to properly coordinate salient sensory inputs with appropriate behavioral responses is fundamentally flawed, and in extreme psychiatric disorders such as schizophrenia, sensory information is nearly shut-out entirely, as nearly all behaviors are driven by internal representations of the world, i.e. hallucinations and delusions. Functional understandings of how these disorders arise are very limited, in part because of the complexity of the underlying processes that produce these behaviors. However, researching internal states in non-humans is challenging for two reasons. 1. These states are often encoded by neuromodulators, but are behaviorally defined based on the probabilities of observed behaviors; the organisms often do not leave a record of their intent. To simplify analysis, these assays often investigate binary states such as satiety vs. hunger, arousal vs. not aroused, even though natural behaviors are not necessarily bimodal. 2. How neuromodulators alter specific synaptic relationships in a circuit is daunting in unannotated, complex circuits. However, both of these challenges can be addressed by using the right model system. Animals that build structures in defined phases, such as orb-weaving spiders, leave a physical record of their behavioral intent, thus simplifying the definition of behavioral states. These paradigms are also more ethologically relevant, as the animals are allowed to perform complex behaviors without the need for learning an artificial binary tasks. Likewise, small animals like C. elegans have defined and annotated synaptic topologies that can be investigated under different neuromodulatory contexts, providing us with an unparalleled ability to understand neuromodulatory rewiring with single neuron resolution. More specifically, how neuron computations like synergistic integration are altered can be addressed in explicitly defined neurons and synapses. My long-term goal is to understand how internal states organize behavior, and more explicitly, how the neuromodulators that define these states alter the function of neuronal circuits. We aim to make two key advancements in the field. 1) We will build a neuromodulatory map of how these pathways coordinate multiple behaviors beyond a binary paradigm. 2) We will quantify how internal states modulate synergistic coupling between neurons.